Many obstacles have impeded neuroimaging research on childhood brain disorders. These include difficulties in obtaining data from children given their limited ability to comply with procedures, the lack of an adequate normative data base with which to characterize normal brain development and against which to identify and measure aberrant brain development, the lack of adequate image analysis tools for characterizing developmental brain changes, and a lack of an adequate means for sharing data analytic tools. Four NIH Institutes (NICHD, NIMH, NINDS, and NIDA) have jointly sponsored a multi-center study to learn more about how the brain develops in typical, healthy children and adolescents. This study is enrolling approximately 500 children, ranging from infancy to young adulthood, who are being seen at different time points over a six-year period at several clinical centers around the US. MRIs of the brain are being performed and the imaging findings are being related to the results of standardized neuropsycological tests. This project serves several purposes: (1) to facilitate the study of normal human brain development; (2) to provide a necessary, representative, and reliable source of normal control data for studies of children with brain disorders and diseases; (3) to provide growth curves for normal brain anatomy; and (4) to aid in the development of pediatric neuroimaging tools. More information regarding this project can be found at the: "MRI Study of Normal Brain Development" website: http://www.brain-child.org/. Our role is to serve as a Diffusion Tensor Data Processing Center (DPC). This entails processing and analyzing all diffusion tensor magnetic resonance (DT-MRI) data that is being acquired by the various Centers during the course of the study. During the last year we began developing the DT-MRI data processing pipeline. Specifically we developed procedures for sorting, displaying, and co-registering diffusion weighted images. The image registration procedure accomplishes the tasks of removing the effects of subject motion and eddy current distortion as well as aligning the images to a given template with only one interpolation step, ensuring minimal quality loss. We also proposed a new strategy for robust estimation of the diffusion tensor and quantities derived from it. Finally we addressed the task of registering DT-MRI data to other structural MRI data contained in the database.